Neuronal message passing using Mean-field, Bethe, and Marginal approximations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Thomas Parr, Dimitrije Markovic, Stefan J. Kiebel, Karl J. Friston

ABSTRACT

Neuronal computations rely upon local interactions across synapses. For a neuronal network to perform inference, it must integrate information from locally computed messages that are propagated among elements of that network. We review the form of two popular (Bayesian) message passing schemes and consider their plausibility as descriptions of inference in biological networks. These are variational message passing and belief propagation - each of which is derived from a free energy functional that relies upon different approximations (mean-field and Bethe respectively). We begin with an overview of these schemes and illustrate the form of the messages required to perform inference using Hidden Markov Models as generative models. Throughout, we use factor graphs to show the form of the generative models and of the messages they entail. We consider how these messages might manifest neuronally and simulate the inferences they perform. While variational message passing offers a simple and neuronally plausible architecture, it falls short of the inferential performance of belief propagation. In contrast, belief propagation allows exact computation of marginal posteriors at the expense of the architectural simplicity of variational message passing. As a compromise between these two extremes, we offer a third approach - marginal message passing - that features a simple architecture, while approximating the performance of belief propagation. Finally, we link formal considerations to accounts of neurological and psychiatric syndromes in terms of aberrant message passing. More... »

PAGES

1889

References to SciGraph publications

  • 2017-01-31. Experimental evidence for circular inference in schizophrenia in NATURE COMMUNICATIONS
  • 2013-11. Cluster Variation Method in JOM
  • 2010. Generalised Filtering in MATHEMATICAL PROBLEMS IN ENGINEERING
  • 2017-12. Working memory, attention, and salience in active inference in SCIENTIFIC REPORTS
  • 2010-03. Action and behavior: a free-energy formulation in BIOLOGICAL CYBERNETICS
  • 2014-12. Active inference, eye movements and oculomotor delays in BIOLOGICAL CYBERNETICS
  • 2012-10. Active inference and agency: optimal control without cost functions in BIOLOGICAL CYBERNETICS
  • 2017-09. Adults with autism overestimate the volatility of the sensory environment in NATURE NEUROSCIENCE
  • 2018-08. Planning and navigation as active inference in BIOLOGICAL CYBERNETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-38246-3

    DOI

    http://dx.doi.org/10.1038/s41598-018-38246-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112090515

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30760782


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1109", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Neurosciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Wellcome Centre for Human Neuroimaging", 
              "id": "https://www.grid.ac/institutes/grid.450002.3", 
              "name": [
                "Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, London, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Parr", 
            "givenName": "Thomas", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "TU Dresden", 
              "id": "https://www.grid.ac/institutes/grid.4488.0", 
              "name": [
                "Chair of Neuroimaging, Psychology Department, Technische Universit\u00e4t Dresden, Dresden, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Markovic", 
            "givenName": "Dimitrije", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "TU Dresden", 
              "id": "https://www.grid.ac/institutes/grid.4488.0", 
              "name": [
                "Chair of Neuroimaging, Psychology Department, Technische Universit\u00e4t Dresden, Dresden, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kiebel", 
            "givenName": "Stefan J.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Wellcome Centre for Human Neuroimaging", 
              "id": "https://www.grid.ac/institutes/grid.450002.3", 
              "name": [
                "Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, London, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Friston", 
            "givenName": "Karl J.", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.3389/fnhum.2014.00457", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000158606"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1002294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000277281"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tins.2008.02.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000975939"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuron.2015.09.034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002780012"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-014-0620-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003116576", 
              "https://doi.org/10.1007/s00422-014-0620-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-014-0620-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003116576", 
              "https://doi.org/10.1007/s00422-014-0620-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-2789(96)00080-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006069527"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1004643", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009591614"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1155/2010/621670", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010738465", 
              "https://doi.org/10.1155/2010/621670"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tins.2004.10.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011236595"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1109355108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011954154"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mehy.2014.12.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013442014"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco.2009.08-08-837", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014831319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/17588928.2015.1020053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016717264"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11837-013-0738-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019220232", 
              "https://doi.org/10.1007/s11837-013-0738-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1000532", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020860546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fncom.2016.00056", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020987522"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1098/rsif.2015.0037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021059653"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0015554", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021694811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1000092", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021838145"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1002211", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021884205"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neubiorev.2016.06.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022425637"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neubiorev.2016.06.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022425637"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/cercor/12.9.936", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022734809"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.csda.2006.10.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025205851"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fpsyg.2016.01792", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026500986"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0960-9822(03)00135-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026944669"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0960-9822(03)00135-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026944669"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1098/rstb.1980.0090", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027143834"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1098/rstb.2008.0300", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027513127"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fnhum.2014.00302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030976816"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3390/brainsci6040044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031455828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/nous.12062", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033405574"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco.2007.19.5.1344", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035440127"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev.neuro.28.061604.135703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036042742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev.neuro.28.061604.135703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036042742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-012-0512-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036345647", 
              "https://doi.org/10.1007/s00422-012-0512-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fncom.2016.00033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037126706"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco.1995.7.6.1129", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037415753"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/brain/awt257", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037543850"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco_a_00477", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037659250"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(79)90767-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038875225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(79)90767-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038875225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/cercor/bhj132", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040359808"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fncom.2012.00044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041095272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-010-0364-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041570522", 
              "https://doi.org/10.1007/s00422-010-0364-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-010-0364-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041570522", 
              "https://doi.org/10.1007/s00422-010-0364-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuroimage.2011.01.085", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042561394"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0893-6080(02)00058-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042906762"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/089976699300016674", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047642158"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuron.2012.10.038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047848838"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuron.2014.08.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049412345"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cub.2007.03.044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049956023"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fpsyt.2013.00047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050700249"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco_a_00912", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051045352"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fncom.2013.00057", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052722626"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/cercor/13.1.73", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053260837"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1090/s0002-9904-1967-11751-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053709349"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s1461145702002894", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1054932755"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.109.208102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060760599"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.109.208102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060760599"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/18.910573", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061101580"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/5.18626", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061178979"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jproc.2007.896497", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061296742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jproc.2014.2308604", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061297903"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/msp.2004.1267047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061422105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tit.2005.850085", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061650545"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnn.1998.712192", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061716400"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.3662552", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062137462"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1089662", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062448564"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1147/rd.53.0183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063183065"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1176/appi.ajp.162.12.2384", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063496378"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1561/2200000001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068001396"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms14218", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083396240", 
              "https://doi.org/10.1038/ncomms14218"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neubiorev.2017.04.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084844247"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/netn_a_00018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085919168"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nn.4615", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090931040", 
              "https://doi.org/10.1038/nn.4615"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nn.4615", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090931040", 
              "https://doi.org/10.1038/nn.4615"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco_a_00999", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091053448"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.conb.2017.08.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091585313"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-15249-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092483346", 
              "https://doi.org/10.1038/s41598-017-15249-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/cercor/bhx316", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092484209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1098/rsif.2017.0376", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092842769"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0190429", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100221095"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fnhum.2018.00061", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101185193"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-018-0753-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101719541", 
              "https://doi.org/10.1007/s00422-018-0753-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-018-0753-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101719541", 
              "https://doi.org/10.1007/s00422-018-0753-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-018-0753-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101719541", 
              "https://doi.org/10.1007/s00422-018-0753-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2026705", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103068135"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/neco_a_01108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105173486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1613/jair.1933", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105579381"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuron.2018.07.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105857862"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.2517-6161.1977.tb01600.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110458051"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.2517-6161.1977.tb01600.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110458051"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-12", 
        "datePublishedReg": "2019-12-01", 
        "description": "Neuronal computations rely upon local interactions across synapses. For a neuronal network to perform inference, it must integrate information from locally computed messages that are propagated among elements of that network. We review the form of two popular (Bayesian) message passing schemes and consider their plausibility as descriptions of inference in biological networks. These are variational message passing and belief propagation - each of which is derived from a free energy functional that relies upon different approximations (mean-field and Bethe respectively). We begin with an overview of these schemes and illustrate the form of the messages required to perform inference using Hidden Markov Models as generative models. Throughout, we use factor graphs to show the form of the generative models and of the messages they entail. We consider how these messages might manifest neuronally and simulate the inferences they perform. While variational message passing offers a simple and neuronally plausible architecture, it falls short of the inferential performance of belief propagation. In contrast, belief propagation allows exact computation of marginal posteriors at the expense of the architectural simplicity of variational message passing. As a compromise between these two extremes, we offer a third approach - marginal message passing - that features a simple architecture, while approximating the performance of belief propagation. Finally, we link formal considerations to accounts of neurological and psychiatric syndromes in terms of aberrant message passing.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/s41598-018-38246-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3636357", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1045337", 
            "issn": [
              "2045-2322"
            ], 
            "name": "Scientific Reports", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "9"
          }
        ], 
        "name": "Neuronal message passing using Mean-field, Bethe, and Marginal approximations", 
        "pagination": "1889", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7f35eec5779b64870022bb7d7f3ac41eac33643988b1023ac1d69e69bbbb1c9e"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "30760782"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101563288"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41598-018-38246-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112090515"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41598-018-38246-3", 
          "https://app.dimensions.ai/details/publication/pub.1112090515"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:40", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000346_0000000346/records_99836_00000004.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/s41598-018-38246-3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38246-3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38246-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38246-3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38246-3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    348 TRIPLES      21 PREDICATES      112 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41598-018-38246-3 schema:about anzsrc-for:11
    2 anzsrc-for:1109
    3 schema:author Na6264e6c5f8e48a4ace37c033c3fa1d8
    4 schema:citation sg:pub.10.1007/s00422-010-0364-z
    5 sg:pub.10.1007/s00422-012-0512-8
    6 sg:pub.10.1007/s00422-014-0620-8
    7 sg:pub.10.1007/s00422-018-0753-2
    8 sg:pub.10.1007/s11837-013-0738-5
    9 sg:pub.10.1038/ncomms14218
    10 sg:pub.10.1038/nn.4615
    11 sg:pub.10.1038/s41598-017-15249-0
    12 sg:pub.10.1155/2010/621670
    13 https://doi.org/10.1016/0167-2789(96)00080-2
    14 https://doi.org/10.1016/j.conb.2017.08.012
    15 https://doi.org/10.1016/j.csda.2006.10.028
    16 https://doi.org/10.1016/j.cub.2007.03.044
    17 https://doi.org/10.1016/j.mehy.2014.12.007
    18 https://doi.org/10.1016/j.neubiorev.2016.06.022
    19 https://doi.org/10.1016/j.neubiorev.2017.04.009
    20 https://doi.org/10.1016/j.neuroimage.2011.01.085
    21 https://doi.org/10.1016/j.neuron.2012.10.038
    22 https://doi.org/10.1016/j.neuron.2014.08.001
    23 https://doi.org/10.1016/j.neuron.2015.09.034
    24 https://doi.org/10.1016/j.neuron.2018.07.003
    25 https://doi.org/10.1016/j.tins.2004.10.007
    26 https://doi.org/10.1016/j.tins.2008.02.005
    27 https://doi.org/10.1016/s0140-6736(79)90767-0
    28 https://doi.org/10.1016/s0893-6080(02)00058-8
    29 https://doi.org/10.1016/s0960-9822(03)00135-0
    30 https://doi.org/10.1017/s1461145702002894
    31 https://doi.org/10.1073/pnas.1109355108
    32 https://doi.org/10.1080/17588928.2015.1020053
    33 https://doi.org/10.1090/s0002-9904-1967-11751-8
    34 https://doi.org/10.1093/brain/awt257
    35 https://doi.org/10.1093/cercor/12.9.936
    36 https://doi.org/10.1093/cercor/13.1.73
    37 https://doi.org/10.1093/cercor/bhj132
    38 https://doi.org/10.1093/cercor/bhx316
    39 https://doi.org/10.1098/rsif.2015.0037
    40 https://doi.org/10.1098/rsif.2017.0376
    41 https://doi.org/10.1098/rstb.1980.0090
    42 https://doi.org/10.1098/rstb.2008.0300
    43 https://doi.org/10.1103/physrevlett.109.208102
    44 https://doi.org/10.1109/18.910573
    45 https://doi.org/10.1109/5.18626
    46 https://doi.org/10.1109/jproc.2007.896497
    47 https://doi.org/10.1109/jproc.2014.2308604
    48 https://doi.org/10.1109/msp.2004.1267047
    49 https://doi.org/10.1109/tit.2005.850085
    50 https://doi.org/10.1109/tnn.1998.712192
    51 https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
    52 https://doi.org/10.1111/nous.12062
    53 https://doi.org/10.1115/1.3662552
    54 https://doi.org/10.1126/science.1089662
    55 https://doi.org/10.1146/annurev.neuro.28.061604.135703
    56 https://doi.org/10.1147/rd.53.0183
    57 https://doi.org/10.1162/089976699300016674
    58 https://doi.org/10.1162/neco.1995.7.6.1129
    59 https://doi.org/10.1162/neco.2007.19.5.1344
    60 https://doi.org/10.1162/neco.2009.08-08-837
    61 https://doi.org/10.1162/neco_a_00477
    62 https://doi.org/10.1162/neco_a_00912
    63 https://doi.org/10.1162/neco_a_00999
    64 https://doi.org/10.1162/neco_a_01108
    65 https://doi.org/10.1162/netn_a_00018
    66 https://doi.org/10.1176/appi.ajp.162.12.2384
    67 https://doi.org/10.1371/journal.pcbi.1000092
    68 https://doi.org/10.1371/journal.pcbi.1000532
    69 https://doi.org/10.1371/journal.pcbi.1002211
    70 https://doi.org/10.1371/journal.pcbi.1002294
    71 https://doi.org/10.1371/journal.pcbi.1004643
    72 https://doi.org/10.1371/journal.pone.0015554
    73 https://doi.org/10.1371/journal.pone.0190429
    74 https://doi.org/10.1561/2200000001
    75 https://doi.org/10.1613/jair.1933
    76 https://doi.org/10.2307/2026705
    77 https://doi.org/10.3389/fncom.2012.00044
    78 https://doi.org/10.3389/fncom.2013.00057
    79 https://doi.org/10.3389/fncom.2016.00033
    80 https://doi.org/10.3389/fncom.2016.00056
    81 https://doi.org/10.3389/fnhum.2014.00302
    82 https://doi.org/10.3389/fnhum.2014.00457
    83 https://doi.org/10.3389/fnhum.2018.00061
    84 https://doi.org/10.3389/fpsyg.2016.01792
    85 https://doi.org/10.3389/fpsyt.2013.00047
    86 https://doi.org/10.3390/brainsci6040044
    87 schema:datePublished 2019-12
    88 schema:datePublishedReg 2019-12-01
    89 schema:description Neuronal computations rely upon local interactions across synapses. For a neuronal network to perform inference, it must integrate information from locally computed messages that are propagated among elements of that network. We review the form of two popular (Bayesian) message passing schemes and consider their plausibility as descriptions of inference in biological networks. These are variational message passing and belief propagation - each of which is derived from a free energy functional that relies upon different approximations (mean-field and Bethe respectively). We begin with an overview of these schemes and illustrate the form of the messages required to perform inference using Hidden Markov Models as generative models. Throughout, we use factor graphs to show the form of the generative models and of the messages they entail. We consider how these messages might manifest neuronally and simulate the inferences they perform. While variational message passing offers a simple and neuronally plausible architecture, it falls short of the inferential performance of belief propagation. In contrast, belief propagation allows exact computation of marginal posteriors at the expense of the architectural simplicity of variational message passing. As a compromise between these two extremes, we offer a third approach - marginal message passing - that features a simple architecture, while approximating the performance of belief propagation. Finally, we link formal considerations to accounts of neurological and psychiatric syndromes in terms of aberrant message passing.
    90 schema:genre research_article
    91 schema:inLanguage en
    92 schema:isAccessibleForFree true
    93 schema:isPartOf N11fc3269a94146338e3c72bb2b3232c9
    94 N761aff58b5a446888b86bbf34f380b17
    95 sg:journal.1045337
    96 schema:name Neuronal message passing using Mean-field, Bethe, and Marginal approximations
    97 schema:pagination 1889
    98 schema:productId N28de6403411642dba551deb03ceee54f
    99 N34110c53e0d94d0d927621c1d1d8f24c
    100 Nccf6e5d17e7f4b25997c3f2ff657d7d2
    101 Ne9a591c9d42b4bccb1b5fe12db013f7f
    102 Nefa8354b963c42c0b2a40ab9d399c5d6
    103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112090515
    104 https://doi.org/10.1038/s41598-018-38246-3
    105 schema:sdDatePublished 2019-04-11T09:40
    106 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    107 schema:sdPublisher Nda9c3eec28a448bc9ad96bdb878e3961
    108 schema:url https://www.nature.com/articles/s41598-018-38246-3
    109 sgo:license sg:explorer/license/
    110 sgo:sdDataset articles
    111 rdf:type schema:ScholarlyArticle
    112 N11fc3269a94146338e3c72bb2b3232c9 schema:issueNumber 1
    113 rdf:type schema:PublicationIssue
    114 N28de6403411642dba551deb03ceee54f schema:name readcube_id
    115 schema:value 7f35eec5779b64870022bb7d7f3ac41eac33643988b1023ac1d69e69bbbb1c9e
    116 rdf:type schema:PropertyValue
    117 N2902dc444bf54f25b1a2d2344a2b22b1 rdf:first N6d034e646ada4871a2e8583630d8d28f
    118 rdf:rest rdf:nil
    119 N34110c53e0d94d0d927621c1d1d8f24c schema:name dimensions_id
    120 schema:value pub.1112090515
    121 rdf:type schema:PropertyValue
    122 N4c18e81524b14728ac3bd0c34000c267 schema:affiliation https://www.grid.ac/institutes/grid.4488.0
    123 schema:familyName Kiebel
    124 schema:givenName Stefan J.
    125 rdf:type schema:Person
    126 N4f8f8dac3238443493355e4650cb933b schema:affiliation https://www.grid.ac/institutes/grid.450002.3
    127 schema:familyName Parr
    128 schema:givenName Thomas
    129 rdf:type schema:Person
    130 N5058ae8b459341f08c75adad1906d630 rdf:first N4c18e81524b14728ac3bd0c34000c267
    131 rdf:rest N2902dc444bf54f25b1a2d2344a2b22b1
    132 N6d034e646ada4871a2e8583630d8d28f schema:affiliation https://www.grid.ac/institutes/grid.450002.3
    133 schema:familyName Friston
    134 schema:givenName Karl J.
    135 rdf:type schema:Person
    136 N761aff58b5a446888b86bbf34f380b17 schema:volumeNumber 9
    137 rdf:type schema:PublicationVolume
    138 N91d34e925b774bcdac7092dd915ac210 rdf:first Nb72bb8bb41ef493781f144092d83dcee
    139 rdf:rest N5058ae8b459341f08c75adad1906d630
    140 Na6264e6c5f8e48a4ace37c033c3fa1d8 rdf:first N4f8f8dac3238443493355e4650cb933b
    141 rdf:rest N91d34e925b774bcdac7092dd915ac210
    142 Nb72bb8bb41ef493781f144092d83dcee schema:affiliation https://www.grid.ac/institutes/grid.4488.0
    143 schema:familyName Markovic
    144 schema:givenName Dimitrije
    145 rdf:type schema:Person
    146 Nccf6e5d17e7f4b25997c3f2ff657d7d2 schema:name pubmed_id
    147 schema:value 30760782
    148 rdf:type schema:PropertyValue
    149 Nda9c3eec28a448bc9ad96bdb878e3961 schema:name Springer Nature - SN SciGraph project
    150 rdf:type schema:Organization
    151 Ne9a591c9d42b4bccb1b5fe12db013f7f schema:name nlm_unique_id
    152 schema:value 101563288
    153 rdf:type schema:PropertyValue
    154 Nefa8354b963c42c0b2a40ab9d399c5d6 schema:name doi
    155 schema:value 10.1038/s41598-018-38246-3
    156 rdf:type schema:PropertyValue
    157 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    158 schema:name Medical and Health Sciences
    159 rdf:type schema:DefinedTerm
    160 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
    161 schema:name Neurosciences
    162 rdf:type schema:DefinedTerm
    163 sg:grant.3636357 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-38246-3
    164 rdf:type schema:MonetaryGrant
    165 sg:journal.1045337 schema:issn 2045-2322
    166 schema:name Scientific Reports
    167 rdf:type schema:Periodical
    168 sg:pub.10.1007/s00422-010-0364-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1041570522
    169 https://doi.org/10.1007/s00422-010-0364-z
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1007/s00422-012-0512-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036345647
    172 https://doi.org/10.1007/s00422-012-0512-8
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1007/s00422-014-0620-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003116576
    175 https://doi.org/10.1007/s00422-014-0620-8
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1007/s00422-018-0753-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101719541
    178 https://doi.org/10.1007/s00422-018-0753-2
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/s11837-013-0738-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019220232
    181 https://doi.org/10.1007/s11837-013-0738-5
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1038/ncomms14218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083396240
    184 https://doi.org/10.1038/ncomms14218
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1038/nn.4615 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090931040
    187 https://doi.org/10.1038/nn.4615
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1038/s41598-017-15249-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092483346
    190 https://doi.org/10.1038/s41598-017-15249-0
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1155/2010/621670 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010738465
    193 https://doi.org/10.1155/2010/621670
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1016/0167-2789(96)00080-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006069527
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1016/j.conb.2017.08.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091585313
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1016/j.csda.2006.10.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025205851
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1016/j.cub.2007.03.044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049956023
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1016/j.mehy.2014.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013442014
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1016/j.neubiorev.2016.06.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022425637
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1016/j.neubiorev.2017.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084844247
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1016/j.neuroimage.2011.01.085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042561394
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1016/j.neuron.2012.10.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047848838
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1016/j.neuron.2014.08.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049412345
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1016/j.neuron.2015.09.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002780012
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1016/j.neuron.2018.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105857862
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1016/j.tins.2004.10.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011236595
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1016/j.tins.2008.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000975939
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.1016/s0140-6736(79)90767-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038875225
    224 rdf:type schema:CreativeWork
    225 https://doi.org/10.1016/s0893-6080(02)00058-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042906762
    226 rdf:type schema:CreativeWork
    227 https://doi.org/10.1016/s0960-9822(03)00135-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026944669
    228 rdf:type schema:CreativeWork
    229 https://doi.org/10.1017/s1461145702002894 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054932755
    230 rdf:type schema:CreativeWork
    231 https://doi.org/10.1073/pnas.1109355108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011954154
    232 rdf:type schema:CreativeWork
    233 https://doi.org/10.1080/17588928.2015.1020053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016717264
    234 rdf:type schema:CreativeWork
    235 https://doi.org/10.1090/s0002-9904-1967-11751-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053709349
    236 rdf:type schema:CreativeWork
    237 https://doi.org/10.1093/brain/awt257 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037543850
    238 rdf:type schema:CreativeWork
    239 https://doi.org/10.1093/cercor/12.9.936 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022734809
    240 rdf:type schema:CreativeWork
    241 https://doi.org/10.1093/cercor/13.1.73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053260837
    242 rdf:type schema:CreativeWork
    243 https://doi.org/10.1093/cercor/bhj132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040359808
    244 rdf:type schema:CreativeWork
    245 https://doi.org/10.1093/cercor/bhx316 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092484209
    246 rdf:type schema:CreativeWork
    247 https://doi.org/10.1098/rsif.2015.0037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021059653
    248 rdf:type schema:CreativeWork
    249 https://doi.org/10.1098/rsif.2017.0376 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092842769
    250 rdf:type schema:CreativeWork
    251 https://doi.org/10.1098/rstb.1980.0090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027143834
    252 rdf:type schema:CreativeWork
    253 https://doi.org/10.1098/rstb.2008.0300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027513127
    254 rdf:type schema:CreativeWork
    255 https://doi.org/10.1103/physrevlett.109.208102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060760599
    256 rdf:type schema:CreativeWork
    257 https://doi.org/10.1109/18.910573 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061101580
    258 rdf:type schema:CreativeWork
    259 https://doi.org/10.1109/5.18626 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061178979
    260 rdf:type schema:CreativeWork
    261 https://doi.org/10.1109/jproc.2007.896497 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061296742
    262 rdf:type schema:CreativeWork
    263 https://doi.org/10.1109/jproc.2014.2308604 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061297903
    264 rdf:type schema:CreativeWork
    265 https://doi.org/10.1109/msp.2004.1267047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061422105
    266 rdf:type schema:CreativeWork
    267 https://doi.org/10.1109/tit.2005.850085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061650545
    268 rdf:type schema:CreativeWork
    269 https://doi.org/10.1109/tnn.1998.712192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061716400
    270 rdf:type schema:CreativeWork
    271 https://doi.org/10.1111/j.2517-6161.1977.tb01600.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110458051
    272 rdf:type schema:CreativeWork
    273 https://doi.org/10.1111/nous.12062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033405574
    274 rdf:type schema:CreativeWork
    275 https://doi.org/10.1115/1.3662552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062137462
    276 rdf:type schema:CreativeWork
    277 https://doi.org/10.1126/science.1089662 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062448564
    278 rdf:type schema:CreativeWork
    279 https://doi.org/10.1146/annurev.neuro.28.061604.135703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036042742
    280 rdf:type schema:CreativeWork
    281 https://doi.org/10.1147/rd.53.0183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063183065
    282 rdf:type schema:CreativeWork
    283 https://doi.org/10.1162/089976699300016674 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047642158
    284 rdf:type schema:CreativeWork
    285 https://doi.org/10.1162/neco.1995.7.6.1129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037415753
    286 rdf:type schema:CreativeWork
    287 https://doi.org/10.1162/neco.2007.19.5.1344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035440127
    288 rdf:type schema:CreativeWork
    289 https://doi.org/10.1162/neco.2009.08-08-837 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014831319
    290 rdf:type schema:CreativeWork
    291 https://doi.org/10.1162/neco_a_00477 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037659250
    292 rdf:type schema:CreativeWork
    293 https://doi.org/10.1162/neco_a_00912 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051045352
    294 rdf:type schema:CreativeWork
    295 https://doi.org/10.1162/neco_a_00999 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091053448
    296 rdf:type schema:CreativeWork
    297 https://doi.org/10.1162/neco_a_01108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105173486
    298 rdf:type schema:CreativeWork
    299 https://doi.org/10.1162/netn_a_00018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085919168
    300 rdf:type schema:CreativeWork
    301 https://doi.org/10.1176/appi.ajp.162.12.2384 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063496378
    302 rdf:type schema:CreativeWork
    303 https://doi.org/10.1371/journal.pcbi.1000092 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021838145
    304 rdf:type schema:CreativeWork
    305 https://doi.org/10.1371/journal.pcbi.1000532 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020860546
    306 rdf:type schema:CreativeWork
    307 https://doi.org/10.1371/journal.pcbi.1002211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021884205
    308 rdf:type schema:CreativeWork
    309 https://doi.org/10.1371/journal.pcbi.1002294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000277281
    310 rdf:type schema:CreativeWork
    311 https://doi.org/10.1371/journal.pcbi.1004643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009591614
    312 rdf:type schema:CreativeWork
    313 https://doi.org/10.1371/journal.pone.0015554 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021694811
    314 rdf:type schema:CreativeWork
    315 https://doi.org/10.1371/journal.pone.0190429 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100221095
    316 rdf:type schema:CreativeWork
    317 https://doi.org/10.1561/2200000001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068001396
    318 rdf:type schema:CreativeWork
    319 https://doi.org/10.1613/jair.1933 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105579381
    320 rdf:type schema:CreativeWork
    321 https://doi.org/10.2307/2026705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103068135
    322 rdf:type schema:CreativeWork
    323 https://doi.org/10.3389/fncom.2012.00044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041095272
    324 rdf:type schema:CreativeWork
    325 https://doi.org/10.3389/fncom.2013.00057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052722626
    326 rdf:type schema:CreativeWork
    327 https://doi.org/10.3389/fncom.2016.00033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037126706
    328 rdf:type schema:CreativeWork
    329 https://doi.org/10.3389/fncom.2016.00056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020987522
    330 rdf:type schema:CreativeWork
    331 https://doi.org/10.3389/fnhum.2014.00302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030976816
    332 rdf:type schema:CreativeWork
    333 https://doi.org/10.3389/fnhum.2014.00457 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000158606
    334 rdf:type schema:CreativeWork
    335 https://doi.org/10.3389/fnhum.2018.00061 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101185193
    336 rdf:type schema:CreativeWork
    337 https://doi.org/10.3389/fpsyg.2016.01792 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026500986
    338 rdf:type schema:CreativeWork
    339 https://doi.org/10.3389/fpsyt.2013.00047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050700249
    340 rdf:type schema:CreativeWork
    341 https://doi.org/10.3390/brainsci6040044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031455828
    342 rdf:type schema:CreativeWork
    343 https://www.grid.ac/institutes/grid.4488.0 schema:alternateName TU Dresden
    344 schema:name Chair of Neuroimaging, Psychology Department, Technische Universität Dresden, Dresden, Germany
    345 rdf:type schema:Organization
    346 https://www.grid.ac/institutes/grid.450002.3 schema:alternateName Wellcome Centre for Human Neuroimaging
    347 schema:name Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, London, UK
    348 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...